Abstract: I document the richness of CEO compensation packages and show that boards learn about the desirability of the many complex package features through observing how these features are associated with firm performance. I first capture the detailed features of plan-based awards for CEOs of the largest U.S. public firms in a vector with more than 1,300 elements. I then demonstrate the complexity of boards' decisions on adding and dropping the detailed features. I hypothesize that boards learn about the efficacy of complex features by observing their correlation with performance---both at their own firms and at other firms. To test these hypotheses, I measure the similarity between any two compensation packages using a metric that assigns a shorter distance to more similar packages. My results support my learning hypotheses: firms that perform well in the current year award similar packages to their CEOs in the following year, whereas firms that perform poorly significantly change their packages in the following year; moreover, firms adjust their own CEO compensation packages to be more similar to that of well-performing firms, and less similar to that of poorly performing firms. These results hold after controlling for the effects from compensation peer firms, compensation-consultant sharing firms, board interlocking firms, and product market peers. I further show that a focal firm experiences better performance when its CEO compensation package becomes more similar to those used by its well-performing compensation benchmark firms. This paper demonstrates the importance of capturing the multi-dimensional details of CEO plan-based awards and studying changes in compensation packages in a holistic manner.

Abstract: We show theoretically that when Bayesian investors face time-series uncertainty about assets' risk exposures, differences in their priors affect the pricing of risk in the cross-section: different priors for the same asset can generate differences in perceived risk exposures, and thereby differences in required returns. The main testable implication is that the relation between required return and risk factor betas is steeper under a low-beta prior than under a high-beta prior. Using novel proxies for investors' priors about assets' exposures to risk factors, we find strong empirical support for our main prediction. Our results have important implications for understanding how prior-induced parameter uncertainty affects asset returns.

Abstract: Size threshold-based regulatory requirements are pervasive, but little is known about how they affect the merger and acquisition (M&A) behavior of firms around the thresholds. M&As cause discrete increases in size, so we hypothesize changes in firms’ M&A behavior near regulatory size thresholds. Our identification strategy relies on size thresholds imposed by the Dodd-Frank Act. We develop a novel research design that estimates indirect treatment effects for banks just below the thresholds. We find strong evidence of indirect treatment effects on M&A behavior. Our results also illustrate the limitations of a standard difference-in-differences approach to studying events that involve size thresholds.

• Do Maximizer Executives Make Better Acquisitions? Evidence from CEO First Marriage Age

Abstract: Relatively little is known about what determines the length of time that CEOs take in making major decisions. One plausible determinant is being a maximizer, which is a personality trait indicating that one seeks first best choices even if doing so requires significantly more time. I use age of first marriage as a proxy for the extent to which CEOs are maximizers. The results show that CEOs who marry later wait a longer time before announcing their first important M&A deals, and that the market responds more positively to deals announced by CEOs who marry later. These results are consistent with the findings that maximizers, who take more time to carefully decide, generate objectively better outcomes.